The neural genome interprets a diversity of electrical firing patterns over timescales of seconds to minutes, making decisions that last a lifetime. There is a fundamental gap in understanding the algorithms and gene regulatory mechanisms by which the genome makes these decisions. The continued existence of this gap is a serious problem; as long as it persists, it will remain difficult or impossible to understand how fear and anxiety become hardwired into the brains of those suffering from anxiety-related disorders. The long-term strategy to address this problem is to take a synergistic two-pronged approach: first applying genomics and systems biology strategies to understand how neural activity is interpreted by the genome and second using this understanding to define the circuitry of fear memory in the cortex. The objective here is to define the algorithms by which the genome interprets a diversity of neural firing patterns, as well as to identify mechanisms by which these algorithms are encoded in cis- and trans-regulatory logic. In the proposed work, we will test the central hypothesis that different patterns of neural activity activate different complements of transcription factors, in turn driving distinct subprograms of activity-regulated genes. In support of this hypothesis, our considerable preliminary data reveal that different classes of activity-regulated genes interpret activity using fundamentally different algorithms. The rationale behind the proposed research is that its successful completion will aid in the next step of developing of functional genetic methods for labeling and manipulating neurons based on defined, quantitative responses to particular experiences. Guided by strong preliminary data, this hypothesis will be tested by pursuing three specific aims: (1) Determining the genome-wide algorithms used by the genome to interpret neural activity; (2) Distinguishing trans-regulatory mechanisms that allow different inducible genes to interpret activity differently; and (3) Functionally evaluating thousands of enhancers and promoters to identify cis-regulatory mechanisms that allow different genes to interpret activity differently. The contribution here is significant because it will resul in an immediate paradigm shift within the field of neural activity-dependent plasticity, establishing that just as synapses and dendrites process chemical and electrical signals, the neural genome is also a quantitative signal-processing machine. It is innovative, because it represents a departure from single-gene and screening approaches to address the computational logic of a genomic signal-processing system. We emphasize that the proposed work is not a screen to make lists or find genes; we've already found them. Instead it is an effort to identify how genomic algorithms are encoded in genome-wide regulatory logic.